EvoGeno Final Project
Jay English, James Forster, and Margo Weber
2024-05-17
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## Guangchuang Yu, David Smith, Huachen Zhu, Yi Guan, Tommy Tsan-Yuk Lam.
## ggtree: an R package for visualization and annotation of phylogenetic
## trees with their covariates and other associated data. Methods in
## Ecology and Evolution. 2017, 8(1):28-36. doi:10.1111/2041-210X.12628
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## Guangchuang Yu. Data Integration, Manipulation and Visualization of
## Phylogenetic Trees (1st edition). Chapman and Hall/CRC. 2022,
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## G Yu. Data Integration, Manipulation and Visualization of Phylogenetic
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## LG Wang, TTY Lam, S Xu, Z Dai, L Zhou, T Feng, P Guo, CW Dunn, BR
## Jones, T Bradley, H Zhu, Y Guan, Y Jiang, G Yu. treeio: an R package
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## associated data. Molecular Biology and Evolution. 2020, 37(2):599-603.
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## Guangchuang Yu. Data Integration, Manipulation and Visualization of
## Phylogenetic Trees (1st edition). Chapman and Hall/CRC. 2022,
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## Guangchuang Yu, Tommy Tsan-Yuk Lam, Huachen Zhu, Yi Guan. Two methods
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## Zhan, T Wu, E Hu, Y Jiang, X Bo, G Yu. ggtreeExtra: Compact
## visualization of richly annotated phylogenetic data. Molecular Biology
## and Evolution. 2021, 38(9):4039-4042. doi: 10.1093/molbev/msab166
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## Guangchuang Yu, David Smith, Huachen Zhu, Yi Guan, Tommy Tsan-Yuk Lam.
## ggtree: an R package for visualization and annotation of phylogenetic
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There are a diverse array of microorganisms found across Earth, and they play a vital role in various ecosystems. There is an ongoing effort in the microbiologist community to find, classify, and study these microorganisms, both to determine their importance in ecology and to monitor for any possible pathogenic outbreaks. Although there has been a surge in data related to microbiotic research, finding ways to take these large datasets and analyze them in a meaningful way has been challenging. Here, we present analysis of newly-deposited data from the National Ecological Observatory Network (NEON) using open-source packages in R.
2 Motivating Reasons
Studying microbial organisms is beneficial for many reasons. Much of modern medicine is owed to the study of these branches of life, as antibiotics are naturally found throughout the microbial universe as these organisms evolve to compete with one another. As such, studying microbes in their natural environment directly benefits mankind. Also important is the ecology of these organisms. Humans are not ecologically isolated; our actions change the environment, which can have an impact to organisms throughout the tree of life. As the environment changes, microorganism populations may change in response. This may not only impact our ability to study them for medicinal purposes, but may also change the frequency and location of pathogenic outbreaks detrimental to societies. As such, monitoring of microbial life can provide predictive power for coming outbreaks, allowing governments to prepare and react quickly to disease outbreaks. Both ecological and microbial studies, however, generate large amounts of data which can be difficult to parse.
With advances in computing, languages have been simplified and programs have been developed to make studying large data sets easier. Open-source languages such as R help to bridge the gap between computer science, which focuses on programming to handle data efficiently, and biology, which generates data from raw samples and experimentation. Using these tools, bioinformaticists seek to yield actionable results from unyieldingly large sets of data. Scientists are increasingly sharing the data they generate into large databases, which provide an ample pool to study with bioinformatic techniques. In this project, we have analyzed one such data set from NEON to identify the microbiome represented at one site in Texas, the Lyndon B. Johnson National Grasslands, and to hone in specifically on Actinomycetota, a large, diverse phylum of bacteria with many medicinal and pathogenic uses.
3 Introduction
3.1 The Lyndon B. Johnson National Grassland
Long through history, the lands that would become the LBJ National Grasslands were favorite hunting grounds for native tribes such as the Cherokee, Creek, Seminole, Waco, and Kickapood (Dferriero 2021). In fact, the earliest discovered human artifacts in North America, namely remains of animals, seeds, and various others were discovered not far from this region (“Fort Worth History” n.d.; “Human History” 2023).
In the 1700s and 1800s, the Comanche commanded this region after aquiring horses and beginning a nomadic war tribe-like lifestyle [noauthor_fort_nodate; Association (n.d.)]. In 1843, negotiations between these and other tribes with the likes of Sam Houston’s deligate generals, Edward J. Tarrant and George W. Terrell saw the natives relocated to territory west of a line through the future site of Fort Worth, giving this town its famous slogan “Where the West Begins”… Not because of its cowboy culture, but rather because this is what the line was referred to as. The second president of Texas, Mirabeau Lamar, did not recognize these treaties for peace, and called for the “total extinction or total repulsion” of the natives as white settlement in the region expanded [noauthor_human_2023; “Native American Relations in Texas Exhibit - Currently Being Refreshed. Please Check Back Later. TSLAC” (n.d.)].
Ranching has always been historically associated with Texas, and Fort Worth was home to some of the largest cattle trades in the world. Ranchers would drive their cattle from throughout the Great Plains to be sold or traded at the Fort Worth stockyard (“Home Fort Worth Stockyards” n.d.).
As such, the grasslands surrounding Fort Worth became essential grazing lands along the final legs of cattle drives. The approximately 20,250 acres surrounding Black Creek Lake Northwest of Fort Worth was soon worked extensively by cattle drivers and homesteaders alike. With a steady influx of homesteaders across the great plains, land mismanagement become a major problem, eventually culminating in the dust bowl, which devestated the natural resources of the region (“Human History” 2023).
President Hoover then began researching agricultural remedies, and his successor, Franklin D. Roosevelt ended land giveaways and began federal management of certain natural lands, including these regions. With well-placed fencing, rotation, and grazing restrictions, vegetation returned to the region around mid-century (“Human History” 2023).
Today, the LBJ National Grasslands are federally managed multiple-use areas. They are protected from being developed or plowed, but still allow limited drilling for the oil and gas industry, grazing for ranching, hunting, camping, and general tourism (“National Forests and Grasslands in Texas - Districts” n.d.).
3.2 National Grasslands LBJ MAGs Overview
NEON_MAGs <- read_csv("data/GOLD_Study_ID_Gs0161344_NEON_2024_4_21.csv") %>%
# remove columns that are not needed for data analysis
select(-c(`GOLD Study ID`, `Bin Methods`, `Created By`, `Date Added`, `Bin Lineage`)) %>%
# create a new column with the Assembly Type
mutate("Assembly Type" = case_when(`Genome Name` == "NEON combined assembly" ~ `Genome Name`,
TRUE ~ "Individual")) %>%
mutate_at("Assembly Type", str_replace, "NEON combined assembly", "Combined") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "d__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "p__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "c__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "o__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "f__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "g__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "s__", "") %>%
separate(`GTDB-Tk Taxonomy Lineage`, c("Domain", "Phylum", "Class", "Order", "Family", "Genus", "Species"), ";", remove = FALSE) %>%
mutate_at("Domain", na_if,"") %>%
mutate_at("Phylum", na_if,"") %>%
mutate_at("Class", na_if,"") %>%
mutate_at("Order", na_if,"") %>%
mutate_at("Family", na_if,"") %>%
mutate_at("Genus", na_if,"") %>%
mutate_at("Species", na_if,"") %>%
# Get rid of the the common string "Soil microbial communities from "
mutate_at("Genome Name", str_replace, "Terrestrial soil microbial communities from ", "") %>%
# Use the first `-` to split the column in two
separate(`Genome Name`, c("Site","Sample Name"), " - ") %>%
# Get rid of the the common string "S-comp-1"
mutate_at("Sample Name", str_replace, "-comp-1", "") %>%
# separate the Sample Name into Site ID and plot info
separate(`Sample Name`, c("Site ID","subplot.layer.date"), "_", remove = FALSE,) %>%
# separate the plot info into 3 columns
separate(`subplot.layer.date`, c("Subplot", "Layer", "Date"), "-") ## Rows: 1754 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): Bin ID, Genome Name, Bin Quality, Bin Lineage, GTDB-Tk Taxonomy L...
## dbl (10): IMG Genome ID, Bin Completeness, Bin Contamination, Total Number ...
## date (1): Date Added
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 624 rows [1131, 1132,
## 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145,
## 1146, 1147, 1148, 1149, 1150, ...].Project_MAGs_LBJ %>%
ggplot(aes(x = fct_rev(fct_infreq(Phylum)))) +
geom_bar(color = "cyan") +
labs(title = "Phylum Counts for MAGs at National Grasslands LBJ, Texas, USA", x = "Phylum", y = "Number of MAGs") +
theme(axis.text.x = element_text(size = 15, angle = 0, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 15),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = 2))+
coord_flip() 
Clearly, our Phylum, Actinobacteriota, makes up the highest percentage of bacteria MAGs at the site! We can also assess the different classes that make up the bacterial MAGs at LBJ:
Project_MAGs_LBJ %>%
ggplot(aes(x = fct_rev(fct_infreq(Class)))) +
geom_bar(color = "cyan") +
labs(title = "Class Counts for MAGs at National Grasslands LBJ, Texas, USA", x = "Class", y = "Number of MAGs") +
theme(axis.text.x = element_text(size = 15, angle = 0, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = -2))+
coord_flip() 
Along with viewing the National Grasslands LBJ Mags in table mode:
Now, lets delve in and take a closer look at our Phylum, Actinomycetota. We can first assess its abundance at each site:
3.3 Abundance of Actinobacteriota at NEON Sites
NEON_MAGs %>%
filter(Phylum == "Actinomycetota") %>%
ggplot(aes(x = fct_rev(fct_infreq(`Site ID`)))) +
geom_bar(color = "cyan") +
labs(title = "Abundance of Actinomycetota at NEON Sites", x = "Site", y = "Number of MAGs") +
theme(axis.text.x = element_text(size = 15, angle = 0, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = 1.5))+
coord_flip() 
Interestingly, actinobacteriota is found most predominantly at National Grasslands LBJ, our site! We can also take a look and see some of the lower level taxonomic breakdown of these actinobacteriota:
3.3.1 Taxonomic Breakdown of Actinobacteriota at Sites
3.3.1.1 Class
NEON_MAGs %>%
filter(Phylum == "Actinomycetota") %>%
filter(Site != "NEON combined assembly") %>%
ggplot(aes(x = fct_rev(fct_infreq(Class)), fill = Site)) +
geom_bar() +
labs(title = "Sub Taxonomy of Actinomycetota, Class (Colored by Site)", x = "Class", y = "Number of MAGs") +
theme(axis.text.x = element_text(size = 15, angle = 0, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = 1.5))+
coord_flip() 
3.3.1.2 Order
NEON_MAGs %>%
filter(Phylum == "Actinomycetota") %>%
filter(Site != "NEON combined assembly") %>%
ggplot(aes(x = fct_rev(fct_infreq(Order)), fill = Site)) +
geom_bar() +
labs(title = "Sub Taxonomy of Actinomycetota, Order (Colored by Site)", x = "Order", y = "Number of MAGs") +
theme(axis.text.x = element_text(size = 15, angle = 0, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = 1.5))+
coord_flip() 
3.3.1.3 Family
NEON_MAGs %>%
filter(Phylum == "Actinomycetota") %>%
filter(Site != "NEON combined assembly") %>%
ggplot(aes(x = fct_rev(fct_infreq(Family)), fill = Site)) +
geom_bar() +
labs(title = "Sub Taxonomy of Actinomycetota, Family (Colored by Site)", x = "Family", y = "Number of MAGs") +
theme(axis.text.x = element_text(size = 15, angle = 0, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = 1.5))+
coord_flip() 
This gives us alot of useful information. However, going further down the taxonomy might give us too much information! So we will stop at family. Although if we wanted, we could determine even the species of each actinobacteriota, colored by site.
3.4 IMG Data for National Grasslands LBJ, Filtered by Re-annotation
## Rows: 176 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (13): Domain, Sequencing Status, Study Name, Genome Name / Sample Name, ...
## dbl (4): taxon_oid, IMG Genome ID, Genome Size * assembled, Gene Count * ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.3.5 Actinomycetota
Actinobacteria, refered to as Actinomycetota in these data, are diverse, gram positive bacteria found in both aquatic and terrestrial environments (Servin et al. 2008). In soil, they are decomposers of organic matter and play a key role in the carbon cycle. This niche is likely distinct from decomposers such as fungi, as they often form symbiotic relationships with plants as nitrogen fixers (Kakoi et al. 2014).
Interestingly, for a long time Actinomycetota colonies were believed to be fungi as they form extensive mycelia. This is how they were actually named (myc being derived from the Greek “myket” for “mushroom”) (Buchanan 1917).
Certain Actinomycetota are important members of the human microbiome. In fact, the genus Bifidobacterium are the most common bacteria in the human infant microbiome (Turroni et al. 2012). In the intestines, bifidobacteria help maintain the mucosal barrier and play a key role in reduction of inflammation by reducing lipopolysacharides in the GI tract (Pinzone et al. 2012).
Actinobacteria are also relevant to human disease. Some members of the genus Mycobacterium, for instance, are pathogenic and are the cause of diseases such as tuberculosis, leprosy, diptheria, vaginosis, and others (Lewin et al. 2016). Still others from the genus Steptomyces are a major source of common antibiotics used in medicine (Lima Procópio et al. 2012).
3.6 Acidobacteriota
Acidobacteriota or Acidobacteria is on of the most abundant, diverse, and underrepresented of soil microbes, a bacterial phyla that largely occupies soils and peatlands across a multitude of ecosystems Kielak et al. (2016). Acidiobacteria have a diverse array of metabolic pathways, allowing for thier surival and proliferation across biomes. Most speies are aerobic or microaerophilic, and some are facultative anaerobic bacteria (Kielak et al. 2016).
It has been found that they even maintain genes permitting them to outcompete established colonies and rapidly form symbiotic relationships with rhiziods(Kalam et al. 2020). In fact, some Acidobacteriota are H2-oxidizing bacteria, that have been found to inhabit grassland and forest soil biomes due to this metabolic ability (Giguere 2020). In fact, this phylum potentially contributes to the major sink of global H2, contributing to their prevalence and biodiversity. This is one attribute that permits, Acidobacteria to confer the metabolic flexibility needed to proliferate over a range of soil conditions and biomes (Crits-Christoph et al. 2022).
This phyla is little studies because of difficulty culviating these bacteria on traditional media (Kalam et al. 2020). Therefore, culture-independent approaches have been devised to investigate this phyla, making study of the phyla neglected and knowledge of its ecological roles limited.
4 Methods
Data tables containing sequencing, site information, metagenomonic, and soil chemistry data sets from NEON were downloaded from the Integrated Microbial Genomes (IMG) database. These data were analyzed using various R packages (listed below) to identify site-specific taxa representation, as well as a focus on one specific phylum (Actinobaxteriota). Code remains shown in this report so results may be easily replicated.
Packages utilized:
tidyverse knitr DT plotly scales ggeasy ggtree TDbook ggimage rphylopic treeio tidytree ape TreeTools phytools ggnewscale ggtreeExtr ggstar
<center # Results ## Actinomycetota distribution across all sites
NEON_MAGs <- read_csv("data/GOLD_Study_ID_Gs0161344_NEON_2024_4_21.csv") %>%
# remove columns that are not needed for data analysis
select(-c(`GOLD Study ID`, `Bin Methods`, `Created By`, `Date Added`, `Bin Lineage`)) %>%
# create a new column with the Assembly Type
mutate("Assembly Type" = case_when(`Genome Name` == "NEON combined assembly" ~ `Genome Name`,
TRUE ~ "Individual")) %>%
mutate_at("Assembly Type", str_replace, "NEON combined assembly", "Combined") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "d__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "p__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "c__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "o__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "f__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "g__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "s__", "") %>%
separate(`GTDB-Tk Taxonomy Lineage`, c("Domain", "Phylum", "Class", "Order", "Family", "Genus", "Species"), ";", remove = FALSE) %>%
mutate_at("Domain", na_if,"") %>%
mutate_at("Phylum", na_if,"") %>%
mutate_at("Class", na_if,"") %>%
mutate_at("Order", na_if,"") %>%
mutate_at("Family", na_if,"") %>%
mutate_at("Genus", na_if,"") %>%
mutate_at("Species", na_if,"") %>%
# Get rid of the the common string "Soil microbial communities from "
mutate_at("Genome Name", str_replace, "Terrestrial soil microbial communities from ", "") %>%
# Use the first `-` to split the column in two
separate(`Genome Name`, c("Site","Sample Name"), " - ") %>%
# Get rid of the the common string "S-comp-1"
mutate_at("Sample Name", str_replace, "-comp-1", "") %>%
# separate the Sample Name into Site ID and plot info
separate(`Sample Name`, c("Site ID","subplot.layer.date"), "_", remove = FALSE,) %>%
# separate the plot info into 3 columns
separate(`subplot.layer.date`, c("Subplot", "Layer", "Date"), "-") ## Rows: 1754 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): Bin ID, Genome Name, Bin Quality, Bin Lineage, GTDB-Tk Taxonomy L...
## dbl (10): IMG Genome ID, Bin Completeness, Bin Contamination, Total Number ...
## date (1): Date Added
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 624 rows [1131, 1132,
## 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145,
## 1146, 1147, 1148, 1149, 1150, ...].actino %>%
ggplot(aes(x = fct_rev(fct_infreq(Site)), fill = Class)) +
geom_bar(position = position_dodge2(width = 0.5, preserve = "single")) +
theme(legend.position = "bottom") +
theme(legend.justification = "left") +
theme(legend.key.size = unit( 0.1, 'cm')) +
theme(legend.key.height = unit(0.1, 'cm')) +
theme(legend.key.width = unit(0.1, 'cm')) +
theme(legend.title = element_text(colour = "black", size = 4, face = "bold")) +
theme(legend.text = element_text(colour = "black", size = 4)) +
theme(legend.box.background = element_rect()) +
theme(legend.box.margin = margin(10, 10, 10, 10)) +
theme(legend.box.just = "left") +
theme( axis.text.x = element_text(size = 6)) +
theme(axis.line.y = element_line(linewidth = 0.25)) +
scale_x_discrete(labels = wrap_format(50)) +
scale_y_continuous(n.breaks = 12) +
theme(axis.text.y = element_text(size = 3)) +
xlab("Site") +
ylab("Count") +
labs(title = str_wrap("Number of Actinobateriota Classes at each Site", width = 30)) +
ggeasy::easy_center_title() +
coord_flip() 
NEON_MAGs %>%
filter(Phylum == "Actinomycetota") %>%
filter(Site != "NEON combined assembly") %>%
ggplot(aes(x = fct_rev(fct_infreq(Family)), fill = Site)) +
geom_bar() +
labs(title = "Sub Taxonomy of Actinobacteriota, Family (Colored by Site)", x = "Family", y = "Number of MAGs") +
theme(axis.text.x = element_text(size = 15, angle = 0, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = 1.5))+
coord_flip() 
actino %>%
ggplot(aes(x = fct_rev(fct_infreq(Site)), fill = Genus)) +
geom_bar(position = position_dodge2(width = 0.5, preserve = "single")) +
guides(fill = guide_legend(ncol = 3)) +
theme(legend.justification = "top") +
theme(legend.position = "left") +
theme(legend.key.size = unit( 0.1, 'cm')) +
theme(legend.key.height = unit(0.1, 'cm')) +
theme(legend.key.width = unit(0.1, 'cm')) +
theme(legend.title = element_text(colour = "black", size = 2, face = "bold")) +
theme(legend.text = element_text(colour = "black", size = 2)) +
theme(legend.box.background = element_rect()) +
theme(legend.box.margin = margin(4, 4, 4, 4)) +
theme(legend.box.just = "left") +
theme( axis.text.x = element_text(size = 4, angle = 90)) +
theme(axis.line.y = element_line(linewidth = 0.25)) +
theme(axis.title = element_text(size = 5)) +
theme(axis.text.y = element_text(size = 4)) +
scale_x_discrete(labels = wrap_format(40)) +
scale_y_continuous(limits = c(0, 20)) +
xlab("Site") +
ylab("Count") +
labs(title = str_wrap("Actinomycetota Genera at each Site", width = 30)) +
ggeasy::easy_plot_title_size(size = 8) +
ggeasy::easy_center_title()## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_bar()`).
NEON_MAGs %>%
filter(Phylum == "Actinomycetota") %>%
filter(Site != "NEON combined assembly") %>%
ggplot(aes(x = fct_rev(fct_infreq(Order)), fill = Site)) +
geom_bar() +
labs(title = "Sub Taxonomy of Actinobacteriota, Order (Colored by Site)", x = "Order", y = "Number of MAGs") +
theme(axis.text.x = element_text(size = 15, angle = 0, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = 1.5))+
coord_flip() 
Looking back at the distribution of Actinomycetota, we see that our
phylum are widely distributed, with most of the classes represented at
the LBJ National Grasslands. We can focus in on this site with some
further visualizations.
4.1 Taxa Found at LBJ National Grasslands
4.1.1 Load in the MAGs and clean up nomenclature conventions.
NEON_MAGs <- read_csv("data/GOLD_Study_ID_Gs0161344_NEON_2024_4_21.csv") %>%
# remove columns that are not needed for data analysis
select(-c(`GOLD Study ID`, `Bin Methods`, `Created By`, `Date Added`, `Bin Lineage`)) %>%
# create a new column with the Assembly Type
mutate("Assembly Type" = case_when(`Genome Name` == "NEON combined assembly" ~ `Genome Name`,
TRUE ~ "Individual")) %>%
mutate_at("Assembly Type", str_replace, "NEON combined assembly", "Combined") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "d__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "p__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "c__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "o__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "f__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "g__", "") %>%
mutate_at("GTDB-Tk Taxonomy Lineage", str_replace, "s__", "") %>%
separate(`GTDB-Tk Taxonomy Lineage`, c("Domain", "Phylum", "Class", "Order", "Family", "Genus", "Species"), ";", remove = FALSE) %>%
mutate_at("Domain", na_if,"") %>%
mutate_at("Phylum", na_if,"") %>%
mutate_at("Class", na_if,"") %>%
mutate_at("Order", na_if,"") %>%
mutate_at("Family", na_if,"") %>%
mutate_at("Genus", na_if,"") %>%
mutate_at("Species", na_if,"") %>%
# Get rid of the the common string "Soil microbial communities from "
mutate_at("Genome Name", str_replace, "Terrestrial soil microbial communities from ", "") %>%
# Use the first `-` to split the column in two
separate(`Genome Name`, c("Site","Sample Name"), " - ") %>%
# Get rid of the the common string "S-comp-1"
mutate_at("Sample Name", str_replace, "-comp-1", "") %>%
# separate the Sample Name into Site ID and plot info
separate(`Sample Name`, c("Site ID","subplot.layer.date"), "_", remove = FALSE,) %>%
# separate the plot info into 3 columns
separate(`subplot.layer.date`, c("Subplot", "Layer", "Date"), "-") ## Rows: 1754 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): Bin ID, Genome Name, Bin Quality, Bin Lineage, GTDB-Tk Taxonomy L...
## dbl (10): IMG Genome ID, Bin Completeness, Bin Contamination, Total Number ...
## date (1): Date Added
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 624 rows [1131, 1132,
## 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145,
## 1146, 1147, 1148, 1149, 1150, ...].4.1.2 Filter for phyla at LBJ
There were many taxa found at the LBJ National grasslands. These can be visualized using a variety of approaches. First, it is useful to get a simple overview of the representation of various phyla using a bar graph.
Project_MAGs_LBJ %>%
ggplot(aes(x = fct_rev(fct_infreq(Phylum)), fill = Class)) +
geom_bar() +
labs(title = "Phylum Counts for MAGs at National Grasslands LBJ, Texas, USA", x = "Phylum", y = "Number of MAGs") +
theme(axis.text.x = element_text(size = 15, angle = 0, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 15),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = .3))+
coord_flip() 
Here, we see that by far the most represented phylum is our phylum, Actinomycetota. The next most represented phylum is Acidobacteriota, which might tell us something about the soil properties at LBJ. We can dig a little bit deeper into this by also looking at the soil chemistry of LBJ.
4.1.3 Load relevant tables
Remove the re-annotation and WREF plot samples from the data set previously exported from IMG
NEON_metagenomes <- read_tsv("data/exported_img_data.tsv") %>%
select(-c(`Domain`, `Sequencing Status`, `Sequencing Center`)) %>%
rename(`Genome Name` = `Genome Name / Sample Name`) %>%
filter(str_detect(`Genome Name`, 're-annotation', negate = T)) %>%
filter(str_detect(`Genome Name`, 'WREF plot', negate = T)) ## Rows: 176 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (13): Domain, Sequencing Status, Study Name, Genome Name / Sample Name, ...
## dbl (4): taxon_oid, IMG Genome ID, Genome Size * assembled, Gene Count * ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Now reformat Genome Name as we did for the above MAG table.
NEON_metagenomes <- NEON_metagenomes %>%
# Get rid of the the common string "Soil microbial communities from "
mutate_at("Genome Name", str_replace, "Terrestrial soil microbial communities from ", "") %>%
# Use the first `-` to split the column in two
separate(`Genome Name`, c("Site","Sample Name"), " - ") %>%
# Get rid of the the common string "-comp-1"
mutate_at("Sample Name", str_replace, "-comp-1", "") %>%
# separate the Sample Name into Site ID and plot info
separate(`Sample Name`, c("Site ID","subplot.layer.date"), "_", remove = FALSE,) %>%
# separate the plot info into 3 columns
separate(`subplot.layer.date`, c("Subplot", "Layer", "Date"), "-")## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [52].Finally, doing the same for the NEON soil chemistry table
NEON Chemistry Data
NEON_chemistry <- read_tsv("data/neon_plot_soilChem1_metadata.tsv") %>%
# remove -COMP from genomicsSampleID
mutate_at("genomicsSampleID", str_replace, "-COMP", "") ## Rows: 87 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (5): genomicsSampleID, siteID, plotID, nlcdClass, horizon
## dbl (11): decimalLatitude, decimalLongitude, elevation, soilTemp, d15N, org...
## date (1): collectionDate
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Now we’ll join these tables together to be able to see the relationship between the MAGs and relevent metadata.
NEON_MAGs_metagenomes_chemistry <- NEON_MAGs %>%
full_join(NEON_metagenomes, by = "Sample Name") %>%
full_join(NEON_chemistry, by = c("Sample Name" = "genomicsSampleID")) %>%
rename("label" = "Bin ID")A useful way for visualizing taxonomic information are phylogenetic trees. We will use these, overlayed with metadata, to parse through these taxa.
First, the trees must be loaded.
tree_arc <- read.tree("data/gtdbtk.ar53.decorated.tree")
tree_bac <- read.tree("data/gtdbtk.bac120.decorated.tree")Next, we’ll filter our merged table for MAGs found at LBJ
In this report, we’ll largely focus on Bacteria, although Archaea are represented in the data. So now, we’ll filter for just Bacteria.
NEON_MAGs_metagenomes_chemistry_CLBJ <- NEON_MAGs_metagenomes_chemistry %>%
filter(`Site ID.x` == "CLBJ") %>%
filter(Domain == "Bacteria")# Create a raw tree
tree_bac_CLBJ_MAGs <-drop.tip(tree_bac,tree_bac$tip.label[-match(CLBJ_MAGs_label, tree_bac$tip.label)])# Visualize the tree
ggtree(tree_bac_CLBJ_MAGs, layout="circular") %<+%
NEON_MAGs_metagenomes_chemistry +
geom_point(mapping=aes(color=Phylum))
Here, we present a different way of visualizing not only the taxa found at LBJ, but also their relatedness to eachother.
This sankey plot provides taxonomic information from coassemblies from all Actinomycetota taxa.
We can make a similar, circular tree with multiple layers of data as well.
Filter blanks from table
NEON_MAGs_metagenomes_chemistry_noblank_CLBJ <- NEON_MAGs_metagenomes_chemistry_CLBJ %>%
rename("AssemblyType" = "Assembly Type") %>%
rename("BinCompleteness" = "Bin Completeness") %>%
rename("BinContamination" = "Bin Contamination") %>%
rename("TotalNumberofBases" = "Total Number of Bases") %>%
rename("EcosystemSubtype" = "Ecosystem Subtype")ggtree(tree_bac_CLBJ_MAGs, layout="circular", branch.length="none") %<+%
NEON_MAGs_metagenomes_chemistry_CLBJ +
geom_point2(mapping=aes(color=`Phylum`, size=`Gene Count`)) +
new_scale_fill() +
geom_fruit(
data=NEON_MAGs_metagenomes_chemistry_noblank_CLBJ,
geom=geom_tile,
mapping=aes(y=label, x=1, fill= AssemblyType),
offset=0.08, # The distance between external layers, default is 0.03 times of x range of tree.
pwidth=0.25 # width of the external layer, default is 0.2 times of x range of tree.
) +
new_scale_fill() +
geom_fruit(
data=NEON_MAGs_metagenomes_chemistry_noblank_CLBJ,
geom=geom_col,
mapping=aes(y=label, x=TotalNumberofBases),
pwidth=0.4,
axis.params=list(
axis="x", # add axis text of the layer.
text.angle=-45, # the text size of axis.
hjust=0 # adjust the horizontal position of text of axis.
),
grid.params=list() # add the grid line of the external bar plot.
) +
theme(#legend.position=c(0.96, 0.5), # the position of legend.
legend.background=element_rect(fill=NA), # the background of legend.
legend.title=element_text(size=7), # the title size of legend.
legend.text=element_text(size=6), # the text size of legend.
legend.spacing.y = unit(0.02, "cm") # the distance of legends (y orientation).
) +
easy_plot_legend_size(20) +
easy_plot_legend_title_size(20)## ! The following column names/name: Site.x, Sample Name, Site ID.x, Subplot.x, Layer.x, Date.x, IMG Genome ID.x, Bin Quality, GTDB-Tk Taxonomy Lineage, Domain, Phylum, Class, Order, Family, Genus, Species, 5s rRNA, 16s rRNA, 23s rRNA, tRNA Genes, Gene Count, Scaffold Count, taxon_oid, Study Name, Site.y, Site ID.y, Subplot.y, Layer.y, Date.y, IMG Genome ID.y, GOLD Study ID, Ecosystem, Ecosystem Category, Ecosystem Type, Specific Ecosystem, Latitude, Longitude, Genome Size * assembled, Gene Count * assembled, siteID, plotID, nlcdClass, decimalLatitude, decimalLongitude, elevation, collectionDate, horizon, soilTemp, d15N, organicd13C, nitrogenPercent, organicCPercent, CNratio, soilInWaterpH, soilInCaClpH are/is the same to tree data, the tree data column names are : label, y, angle, Site.x, Sample Name, Site ID.x, Subplot.x, Layer.x, Date.x, IMG Genome ID.x, Bin Quality, GTDB-Tk Taxonomy Lineage, Domain, Phylum, Class, Order, Family, Genus, Species, Bin Completeness, Bin Contamination, Total Number of Bases, 5s rRNA, 16s rRNA, 23s rRNA, tRNA Genes, Gene Count, Scaffold Count, Assembly Type, taxon_oid, Study Name, Site.y, Site ID.y, Subplot.y, Layer.y, Date.y, IMG Genome ID.y, GOLD Study ID, Ecosystem, Ecosystem Category, Ecosystem Subtype, Ecosystem Type, Specific Ecosystem, Latitude, Longitude, Genome Size * assembled, Gene Count * assembled, siteID, plotID, nlcdClass, decimalLatitude, decimalLongitude, elevation, collectionDate, horizon, soilTemp, d15N, organicd13C, nitrogenPercent, organicCPercent, CNratio, soilInWaterpH, soilInCaClpH.## ! The following column names/name: Site.x, Sample Name, Site ID.x, Subplot.x, Layer.x, Date.x, IMG Genome ID.x, Bin Quality, GTDB-Tk Taxonomy Lineage, Domain, Phylum, Class, Order, Family, Genus, Species, 5s rRNA, 16s rRNA, 23s rRNA, tRNA Genes, Gene Count, Scaffold Count, taxon_oid, Study Name, Site.y, Site ID.y, Subplot.y, Layer.y, Date.y, IMG Genome ID.y, GOLD Study ID, Ecosystem, Ecosystem Category, Ecosystem Type, Specific Ecosystem, Latitude, Longitude, Genome Size * assembled, Gene Count * assembled, siteID, plotID, nlcdClass, decimalLatitude, decimalLongitude, elevation, collectionDate, horizon, soilTemp, d15N, organicd13C, nitrogenPercent, organicCPercent, CNratio, soilInWaterpH, soilInCaClpH are/is the same to tree data, the tree data column names are : label, y, angle, Site.x, Sample Name, Site ID.x, Subplot.x, Layer.x, Date.x, IMG Genome ID.x, Bin Quality, GTDB-Tk Taxonomy Lineage, Domain, Phylum, Class, Order, Family, Genus, Species, Bin Completeness, Bin Contamination, Total Number of Bases, 5s rRNA, 16s rRNA, 23s rRNA, tRNA Genes, Gene Count, Scaffold Count, Assembly Type, taxon_oid, Study Name, Site.y, Site ID.y, Subplot.y, Layer.y, Date.y, IMG Genome ID.y, GOLD Study ID, Ecosystem, Ecosystem Category, Ecosystem Subtype, Ecosystem Type, Specific Ecosystem, Latitude, Longitude, Genome Size * assembled, Gene Count * assembled, siteID, plotID, nlcdClass, decimalLatitude, decimalLongitude, elevation, collectionDate, horizon, soilTemp, d15N, organicd13C, nitrogenPercent, organicCPercent, CNratio, soilInWaterpH, soilInCaClpH, xmaxtmp.## Warning: Removed 190 rows containing missing values or values outside the scale range
## (`geom_point_g_gtree()`).
With this tree, we can easily compare the different phyla found at LBJ, along with their genome sizes. We can see that Actinomycetota have an extreme range of genetic diversity, with members having both very large and small genomes compared to other taxa, as well as a broad range of identified genes. We can also see that all data represented here comes from individual assemblies.
4.2 Mycobacterium
One particular genus of Actinomycetota, mycobacterium, is most notorious for its species which causes the disease tuberculosis. However, there may be non-pathogenic species represented as well.
4.2.1 Filtering relevant taxa
NEON_MAGs_metagenomes_chemistry_myco <- NEON_MAGs_metagenomes_chemistry %>%
filter(Genus == "Mycobacterium")Now we’ll build the tree.
# Create a raw tree
tree_bac_myco <-drop.tip(tree_bac,tree_bac$tip.label[-match(myco_MAGs_label, tree_bac$tip.label)])# Visualize the tree
ggtree(tree_bac_myco, layout= "circular") %<+%
NEON_MAGs_metagenomes_chemistry_myco +
geom_point(mapping=aes(color=Species))
From this tree, we can see that there’s not much data down to the species level. This may be because of incomplete reads, or not enough sequence depth to fully classify in that detail. We’ve also taken a look at the read qualities to try and discern what’s going on.
Here is a datatable with the bin qualities of this genus’ reads.
quality <-
actino %>%
filter(Genus == "Mycobacterium") %>%
count(`Bin Quality`, sort = TRUE)
datatable(quality)We can see here that there are only two high-quality reads. We can see which reads these are with a new table.
actino_myco <- actino %>%
filter(Genus == "Mycobacterium")
actino_myco %>%
ggplot(aes(x = fct_rev(fct_infreq(Species)), fill = `Bin Quality`)) +
geom_bar(position = position_dodge2(width = 0.5, preserve = "single")) +
theme(legend.position = "bottom") +
theme(legend.justification = "left") +
theme(legend.key.size = unit( 0.1, 'cm')) +
theme(legend.key.height = unit(0.1, 'cm')) +
theme(legend.key.width = unit(0.1, 'cm')) +
theme(legend.title = element_text(colour = "black", size = 4, face = "bold")) +
theme(legend.text = element_text(colour = "black", size = 4)) +
theme(legend.box.background = element_rect()) +
theme(legend.box.margin = margin(10, 10, 10, 10)) +
theme(legend.box.just = "left") +
theme( axis.text.x = element_text(size = 6)) +
theme(axis.line.y = element_line(linewidth = 0.25)) +
scale_x_discrete(labels = wrap_format(50)) +
scale_y_continuous(n.breaks = 10) +
theme(axis.text.y = element_text(size = 6)) +
xlab("Species") +
ylab("Count") +
labs(title = str_wrap("Bin Quality of Mycobacterium Reads", width = 30)) +
ggeasy::easy_center_title() +
coord_flip() 
Now we can see that both high quality reads are not able to be
identified as a particular pathogenic species. However, this does not
necessarily mean that these are non-pathogenic species. For this reason,
the regions where these were found should be flagged for further
study.
quality <-
actino_myco %>%
filter(Genus == "Mycobacterium") %>%
select(c(`Site`, `Sample Name`, `Subplot`, `Date`, `Bin Quality`)) %>%
filter(`Bin Quality` == "HQ")
datatable(quality)Both high-quality unidentified reads are found from the same subplot in Watershed, Alaska. To ensure safeguards and early detection of possible pathogenic sources, more samples should be gathered from this location for increased sequencing depth.
5 Actinomycetata
5.1 General Visualization of Actinomycetota
In this section, we will continue our analysis into the general characteristics and visualization of Actinomycetota bacteria, both in all of the NEON sites, as well as honed in on our specific site. We will put particular emphasis on analyzing the specific class and order of bacteria found at the different sites, some of their observed patterns, and interesting information about them.
Below, we take another look at the sub-taxonomy of Actinomycetota into classes, and colored by site:
NEON_MAGs %>%
filter(Phylum == "Actinomycetota") %>%
filter(Site != "NEON combined assembly") %>%
filter(Class != "NA") %>%
ggplot(aes(x = fct_rev(fct_infreq(Class)), fill = Site)) +
geom_bar() +
labs(title = "Sub Taxonomy of Actinomycetota, Class (Colored by Site)", x = "Class", y = "Number of MAGs") +
theme(axis.text.x = element_text(size = 15, angle = 0, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = 1.5))+
coord_flip() 
Here we can see a few very interesting findings in the data. Firstly, Actnomycetota metagenomes appear in five different classes: Thermoleophilia, Actinomycetia, Acidimicrobiia, Rubrobacteria, and the elusive unnamed UBA4738. Particularly interesting is the relative scarcity of the Rubrobacteria class across all the NEON sites. Rubrobacteria is known as a thermophile, able to thrive in very high temperature environments (even above 70 degrees Celsius) (noauthor_desulfuromusa_nodate?), which could possibly explain their relative lack of abundance in the NEON data. We can look out for these extremophile characteristics of Rubrobacteria, as well as Thermoleophilia, in subsequent analyses.
We can also see a further taxonomic breakdown into bacterial Orders colored by site below:
NEON_MAGs %>%
filter(Phylum == "Actinomycetota") %>%
filter(Site != "NEON combined assembly") %>%
ggplot(aes(x = fct_rev(fct_infreq(Order)), fill = Site)) +
geom_bar() +
labs(title = "Sub Taxonomy of Actinomycetota, Order (Colored by Site)", x = "Order", y = "Number of MAGs") +
theme(axis.text.x = element_text(size = 15, angle = 0, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = 1.5))+
coord_flip()
Below, we will begin our analysis into the NEON chemistry characteristics of the Actinomycetota MAGs at the sites, to get a better understanding of the conditions that the bacteria live and thrive in, as well as to find some intersting patterns in the data.
NEON_MAGs_metagenomes_chemistry %>%
filter(Phylum == "Actinomycetota") %>%
filter(Class != "NA") %>%
filter(`Site ID.x` != "NA") %>%
ggplot(aes(x = fct_rev(fct_infreq(`Site ID.x`)), fill = nlcdClass)) +
labs(title = "Abundance of Actinomycetota by Site, Colored by nlcdClass", x = "Site ID", y = "Number of MAGs") +
geom_bar() +
theme(axis.text.x = element_text(size = 15, angle = 45, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = .5), legend.position = "bottom") 
In the data set above, we color the abundance of Actinomycetota, labeled by class, by the nlcdClass, or environmental subtype. We can see that Actinomycetota are found in a variety of different environments, with the most abundant source of Actinomycetota, National Grasslands LBJ, being primarily a deciduous forest. We can see from the nclcd classes that, although there are many different ones, they have similar named characteristics, and are all a type of forest, scrub, or grassland.
Below we provide another visualization of the abundance of Actinomycetota at all the NEON sites. Immediately apparent is the enormous size of the phylum, making up more than 1/3 of all the NEON MAGs discoverd across all the NEON sites.
ggtree(tree_bac, layout="circular", branch.length="none") +
geom_hilight(node=1789, fill="darkviolet", alpha=.6) +
geom_cladelab(node=1789, label="Actinomycetota", align=TRUE,
offset = .5, textcolor='darkviolet', fontsize = 8)
## Phylogenetic Analysis of Actinomycetota at All Sites
Now, we will zoom in on Actinomycetota, analyzing the trees and digging into the class phylogenetics at all NEON sites:
tree_bac_node_Actinomycetota <- Preorder(tree_bac)
tree_Actinomycetota <- Subtree(tree_bac_node_Actinomycetota, 1789)
tree_bac1 <- tree_bac
tree_bac_node_Thermoleophilia <- Preorder(tree_bac1)
tree_Thermoleophilia <- Subtree(tree_bac_node_Thermoleophilia, 2166)ggtree(tree_Actinomycetota, layout="circular") %<+%
NEON_MAGs_metagenomes_chemistry +
xlim(0,25) +
geom_point(mapping=aes(color=Class, shape = `Assembly Type`)) +
theme(legend.text = element_text(size = 12))## Warning: Removed 670 rows containing missing values or values outside the scale range
## (`geom_point()`).
Here, we can see a circular tree view of the Actinommycetota phylum, with the nodes colored by the classes we had discussed earlier. In this view, we can see that although there are 5 classes of Actinomycetota found at all sites, there are still a vast array of phylogenetic branches. By visualizing the tree, we can determine where exactly two phylogentic examples of Actinomycetota from each class diverged.
In this view, we can fully appreciate the relatively low abundance of Rubrobacteiria at the sites, seeing its complete divergence from the 4 other classes early in the tree’s structure.
Below we can see another view of the Class phylogeny of Actinomycetota in a circular tree:
NEON_MAGs_metagenomes_chemistry_Actino <- NEON_MAGs_metagenomes_chemistry %>%
filter(Domain == "Bacteria") %>%
filter(Phylum == "Actinomycetota") %>%
filter(!is.na(Class))
MAGs_Actino_label <- NEON_MAGs_metagenomes_chemistry_Actino$label
tree_bac_MAGs_Actino <-drop.tip(tree_bac,tree_bac$tip.label[-match(MAGs_Actino_label, tree_bac$tip.label)])
ggtree(tree_bac_MAGs_Actino, layout="circular") %<+%
NEON_MAGs_metagenomes_chemistry +
geom_point(mapping=aes(color=Class)) 
To get more information from this tree, we can also color the nodes by Ecosystem subtype, emphasizing the abundacne of Actinomycetota in grasslands, shrublands, and forests. However, there are some examples of Tundra, Desert, and Taiga MAGs as well.
NEON_MAGs_metagenomes_chemistry_Actino <- NEON_MAGs_metagenomes_chemistry %>%
filter(Domain == "Bacteria") %>%
filter(Phylum == "Actinomycetota") %>%
filter(!is.na(`Ecosystem Subtype`))
MAGs_Actino_label <- NEON_MAGs_metagenomes_chemistry_Actino$label
tree_bac_MAGs_Actino <-drop.tip(tree_bac,tree_bac$tip.label[-match(MAGs_Actino_label, tree_bac$tip.label)])
ggtree(tree_bac_MAGs_Actino, layout="circular") %<+%
NEON_MAGs_metagenomes_chemistry +
geom_point(mapping=aes(color=`Ecosystem Subtype`))
5.2 National Grasslands LBJ, and Actinomycetota
Now, lets briefly take a break from analyzing Actinomycetota at all sites, and zoom in on National Grasslands LBJ, or CLBJ. We. will begin, as previously, by assessing the phylogenetic tree.
NEON_MAGs_metagenomes_chemistry_CLBJ <- NEON_MAGs_metagenomes_chemistry %>%
filter(`Site ID.x` == "CLBJ")
NEON_MAGs_metagenomes_chemistry_CLBJ <- NEON_MAGs_metagenomes_chemistry %>%
filter(`Site ID.x` == "CLBJ") %>%
filter(Domain == "Bacteria")
CLBJ_MAGs_label <- NEON_MAGs_metagenomes_chemistry_CLBJ$label
tree_bac_CLBJ_MAGs <-drop.tip(tree_bac,tree_bac$tip.label[-match(CLBJ_MAGs_label, tree_bac$tip.label)])
ggtree(tree_bac_CLBJ_MAGs, layout="circular") %<+%
NEON_MAGs_metagenomes_chemistry +
geom_point(mapping=aes(color=Phylum))
NEON_MAGs %>%
filter(Site != "NEON combined assembly") %>%
filter(`Site ID` == "CLBJ") %>%
ggplot(aes(x = fct_rev(fct_infreq(Phylum)), fill = Class)) +
geom_bar() +
labs(title = "Bacteria MAGs at National Grasslands LBJ" , x = "Phylum", y = "Number of MAGs") +
theme(axis.text.x = element_text(size = 15, angle = 0, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = 1.5))+
coord_flip() 
We can see from the tree and bargraph above that, although a very
diverse site, Actinomycetota is the most abundant phylum of bacteria
found at CLBJ.
As we did in with the Actinomycetota data set from all the sites, we can zoom in on the phylum at CLBJ only, to provide a phylogenetic analysis:
NEON_MAGs_metagenomes_chemistry_CLBJ_Actino <- NEON_MAGs_metagenomes_chemistry %>%
filter(`Site ID.x` == "CLBJ") %>%
filter(Domain == "Bacteria") %>%
filter(Phylum == "Actinomycetota")
CLBJ_MAGs_Actino_label <- NEON_MAGs_metagenomes_chemistry_CLBJ_Actino$label
tree_bac_CLBJ_MAGs_Actino <-drop.tip(tree_bac,tree_bac$tip.label[-match(CLBJ_MAGs_Actino_label, tree_bac$tip.label)])
ggtree(tree_bac_CLBJ_MAGs_Actino, layout="circular") %<+%
NEON_MAGs_metagenomes_chemistry +
geom_point(mapping=aes(color=Class)) 
We can see that, from the tree, Thermoleophilia and Actinomycetia are
the two most abundant classes of Actinomycetota at CLBJ. Interesting to
note is that although there are Rubrobacteria examples in the entire
NEON data, none of them are found at CLBJ. However, we still see the
elusive Class UBA4738, of which there is very little published
information. We can now go further down the phylogenetic tree to assess
the Order of the Actinomycetota bacteria found at CLBJ:
NEON_MAGs_metagenomes_chemistry_CLBJ_Actino <- NEON_MAGs_metagenomes_chemistry %>%
filter(`Site ID.x` == "CLBJ") %>%
filter(Domain == "Bacteria") %>%
filter(Phylum == "Actinomycetota")
CLBJ_MAGs_Actino_label <- NEON_MAGs_metagenomes_chemistry_CLBJ_Actino$label
tree_bac_CLBJ_MAGs_Actino <-drop.tip(tree_bac,tree_bac$tip.label[-match(CLBJ_MAGs_Actino_label, tree_bac$tip.label)])
ggtree(tree_bac_CLBJ_MAGs_Actino, layout="circular") %<+%
NEON_MAGs_metagenomes_chemistry +
geom_point(mapping=aes(color=Order)) 
In the above tree, we can see that there is only a single phylogenetic
group of the order Actinomycetales found at CLBJ (shown in brown in the
bottom of the tree). This bacterial order is particularly interesting
and known for its ability to grow in long branches, similar in look to
fungal growth (daur_boosting_2018?).
5.3 Thermoleophilia, an Interesting Class of Actinomycetota
Now, we can delve deeper and focus on a specific class of interesting extremophile bacteria, Thermoleophilia. Thermoleophilia, as we showed in the earlier trees, is one of the most abundant classes of Actinomycetota, both site-wide and at CLBJ specifically. Below, we will assess the phylogenetics of Thermoleophilia:
ggtree(tree_Thermoleophilia, layout="circular") %<+%
NEON_MAGs_metagenomes_chemistry +
xlim(0,25) +
geom_point(mapping=aes(color=Order, shape = `Assembly Type`))+
theme(legend.text = element_text(size = 12))## Warning: Removed 293 rows containing missing values or values outside the scale range
## (`geom_point()`).
In the above tree, we get a comprehensive look at the Order of Thermoleophilia found across all NEON sites. Of particular interest is the extreme abundance of Solirubrobacterales, as well as only three divergent branches of the order Miltoncostaeales, which diverged from the order Gaiellales relatively late in the tree structure. Since there is very little published information on this order, it is probable that Miltoncostaeales only very recently was classified into a new order separate to that of Gaiellales. This is a testament to the rapidly changing and evolving field of metagenomics and bacterial phylogenetics.
We will now perform the same analysis focusing on CLBJ specifically:
Now zoom in on CLBJ:
NEON_MAGs_metagenomes_chemistry_CLBJ_Actino <- NEON_MAGs_metagenomes_chemistry %>%
filter(`Site ID.x` == "CLBJ") %>%
filter(Domain == "Bacteria") %>%
filter(Phylum == "Actinomycetota") %>%
filter(Class == "Thermoleophilia")
CLBJ_MAGs_Actino_label <- NEON_MAGs_metagenomes_chemistry_CLBJ_Actino$label
tree_bac_CLBJ_MAGs_Actino <-drop.tip(tree_bac,tree_bac$tip.label[-match(CLBJ_MAGs_Actino_label, tree_bac$tip.label)])
ggtree(tree_bac_CLBJ_MAGs_Actino, layout="circular") %<+%
NEON_MAGs_metagenomes_chemistry +
geom_point(mapping=aes(color=Order)) 
At CLBJ, we only see the two orders Gaiellales and Solirubrobacterales.
Now lets assess the families:
NEON_MAGs_metagenomes_chemistry_CLBJ_Actino <- NEON_MAGs_metagenomes_chemistry %>%
filter(`Site ID.x` == "CLBJ") %>%
filter(Domain == "Bacteria") %>%
filter(Phylum == "Actinomycetota") %>%
filter(Class == "Thermoleophilia")
CLBJ_MAGs_Actino_label <- NEON_MAGs_metagenomes_chemistry_CLBJ_Actino$label
tree_bac_CLBJ_MAGs_Actino <-drop.tip(tree_bac,tree_bac$tip.label[-match(CLBJ_MAGs_Actino_label, tree_bac$tip.label)])
ggtree(tree_bac_CLBJ_MAGs_Actino, layout="circular") %<+%
NEON_MAGs_metagenomes_chemistry +
geom_point(mapping=aes(color=Family)) +
easy_all_text_size(size = 8)
Another surprising scarcity is discovered; as there is only one
phylogenetic branch dedicated to the family Thermoleophilaceae at CLBJ.
Furthermore, there is another elusively named phylogeny of the 70-9
family, of which there is very little, if any, published information on.
Now, lets zoom in on the Genus of the above examples:
NEON_MAGs_metagenomes_chemistry_CLBJ_Actino <- NEON_MAGs_metagenomes_chemistry %>%
filter(`Site ID.x` == "CLBJ") %>%
filter(Domain == "Bacteria") %>%
filter(Phylum == "Actinomycetota") %>%
filter(Class == "Thermoleophilia")
CLBJ_MAGs_Actino_label <- NEON_MAGs_metagenomes_chemistry_CLBJ_Actino$label
tree_bac_CLBJ_MAGs_Actino <-drop.tip(tree_bac,tree_bac$tip.label[-match(CLBJ_MAGs_Actino_label, tree_bac$tip.label)])
ggtree(tree_bac_CLBJ_MAGs_Actino, layout="circular") %<+%
NEON_MAGs_metagenomes_chemistry +
geom_point(mapping=aes(color=Genus))
From this tree we can see that the single branch of the family
Thermoleophilaceae is of the genus Conexibacter.
5.4 NEON Chemistry Analysis of Actinomycetota
We will wrap up our analysis of Actinomycetota NEON MAGs by assessing some patterns in the phylogenes with regards to physical and chemical conditions in the NEON chemistry data.
First, we will assess the effect of soil temperature on the abundance of Actinomycetota at all sites, faceted by class:
NEON_MAGs_metagenomes_chemistry %>%
filter(Phylum == "Actinomycetota") %>%
filter(Class != "NA") %>%
ggplot(aes(x=soilTemp, fill=Class )) +
labs(title = "Soil Temperatuer of Actinomycetota, Faceted by Class", x = "Soil Temperature", y = "Number of MAGs") +
geom_histogram(bins = 25) +
theme(axis.text.x = element_text(size = 15, angle = 45, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 15, hjust = .5), legend.position = "bottom") +
facet_wrap(vars(Class), scales = "free", ncol = 2)## Warning: Removed 253 rows containing non-finite outside the scale range
## (`stat_bin()`).
There are a few intersting findings in this data set. Firstly, we see that the class Acidimicrobiia preferentially is found in colder climates, indicative of the peak in MAG numbers around 10 degrees Celsius. Next, we see that, although not abundant, Rubrobacteria align with its thermophile characteristics with a peak in MAG numbers in the higher range of the temperatures measured. Although not close to the max temperature of thermophiles, it is an interesting finding that follows the expected patterns. Finally, we see that a larger portion of the class UBA4738 is found at higher temperatures, certainly compared to the relative abundance of the classes Thermoleophilia, Actinomycetia, and Acidimicrobiia, which prefer more moderate temperatures in the 10-20 degree Celsius range.
Now, we will assess pH differences in the MAGs found in Actinomycetota:
NEON_MAGs_metagenomes_chemistry %>%
filter(Phylum == "Actinomycetota") %>%
filter(Class != "NA") %>%
ggplot(aes(x=soilInWaterpH, fill=Class )) +
labs(title = "Water pH of Actinomycetota, Faceted by Class", x = "pH", y = "Number of MAGs") +
geom_histogram(bins = 25) +
theme(axis.text.x = element_text(size = 15, angle = 45, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 15, hjust = .5), legend.position = "bottom") +
facet_wrap(vars(Class), scales = "free", ncol = 2)## Warning: Removed 253 rows containing non-finite outside the scale range
## (`stat_bin()`).
Here, we can determine that the mysterious class UBA4738, while also preferring higher temperatures, also is more abundant at high pH values as well. This may suggest UBA4738 as a novel thermophile class, however, more data and study into this class is needed to confirm this.
Let’s now compare all the classes of Actinomycetota on one graph and assess the pH differneces:
NEON_MAGs_metagenomes_chemistry %>%
filter(Phylum == "Actinomycetota") %>%
filter(Class != "NA") %>%
ggplot(aes(x=soilInWaterpH, fill=Class )) +
labs(title = "Water pH of Actinomycetota", x = "pH", y = "Number of MAGs") +
geom_histogram(bins = 25) +
theme(axis.text.x = element_text(size = 15, angle = 45, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 17, hjust = .5), legend.position = "bottom") +
annotate("rect", xmin = 5.3, xmax = 9.3, ymin = -.5, ymax = 10.3,
fill = NA, color = "black", size = 1.2)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Removed 253 rows containing non-finite outside the scale range
## (`stat_bin()`).
Above, we clearly see the preferential finding of UBA4738 in more basic
environments, with no examples being found below pH 5, compared to four
of the other classes being found in high numbers below this pH
value.
Finally, we will take one more NEON chemistry visualization of Actinomycetota at all sites, assessing the effect of elevation. While there arent too many trends to find from this data, we get an idea of the relative abundance of each class at different elevations and different sites, which may be of use in the future as more MAGs are generated.
NEON_MAGs_metagenomes_chemistry %>%
filter(Phylum == "Actinomycetota") %>%
filter(Class != "NA") %>%
ggplot(aes(x=elevation, fill=`Site ID.x` )) +
labs(title = "Elevation of Actinomycetota, Faceted by Class", x = "Elevation", y = "Number of MAGs") +
geom_histogram(bins = 25) +
theme(axis.text.x = element_text(size = 15, angle = 45, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 12),
text = element_text(size = 15), plot.title = element_text(size = 15, hjust = .5), legend.position = "bottom") +
facet_wrap(vars(Class), scales = "free", ncol = 2)## Warning: Removed 253 rows containing non-finite outside the scale range
## (`stat_bin()`).
6 Acidobacteriota
6.1 Introduction
Here, we investigated the Acidobacteriota phylum and specifically
prevalence and diversity
across different biomes.
# Search for Phylum or Class to get the node
node_vector_bac = c(tree_bac$tip.label,tree_bac$node.label)
# Search for your Phylum or Class to get the node
node_loc_acid<-match(grep("Acidobacteriota", node_vector_bac, value = TRUE), node_vector_bac)6.1.1 At a Glance
Here we can glean the spectra of biodiversity within the Acidobacteriota phylum. There are an array of biomes with a diversity of bacterial classes and genuses that inhabit those biomes
# First need to preorder tree before extracting.
tree_bac_preorder <- Preorder(tree_bac)
tree_Acidobacteriota <- Subtree(tree_bac_preorder, node_loc_acid)
# Filter NEON table to taxonomic group or site for Acidobacteriota
NEON_MAGs_Acidobacteriota <- NEON_MAGs_metagenomes_chemistry %>%
filter(Phylum == "Acidobacteriota")
datatable(NEON_MAGs_Acidobacteriota)6.2 Analysis
6.2.1 Acidobacteriota Clade within Soil Microbes
ggtree(tree_bac, layout="circular", branch.length="none") +
geom_hilight(node=node_loc_acid, fill="orange", alpha=.6) +
geom_cladelab(node=node_loc_acid, label=" Acidobacteriota", align=TRUE,
offset = 1, textcolor='red', barcolor='orange',
hjust=1, vjust=1.3)
### Ecosystem Subtypes & Classes In taking a closer look at the
Phylum Acidobacteriota as a whole, with special interest in Ecosystem
Subtypes and the classes of Acidobacteriota, we see that five classes
that inhabit eight ecosystem subtypes constitute the entire
Acidobacteriota phylum.
NEON_MAGs_metagenomes_chemistry_Acid <- NEON_MAGs_metagenomes_chemistry %>%
filter(Phylum == "Acidobacteriota",
!is.na(`Class`),
!is.na(`Ecosystem Subtype`))
MAGs_Acid_label <- NEON_MAGs_metagenomes_chemistry_Acid$label
tree_bac_MAGs_Acid <-drop.tip(tree_bac,tree_bac$tip.label[-match(MAGs_Acid_label, tree_bac$tip.label)])
ggtree(tree_bac_MAGs_Acid) %<+%
NEON_MAGs_metagenomes_chemistry +
#geom_tiplab(size = 3) +
xlim(0,0.9) +
geom_point(mapping=aes(color=`Ecosystem Subtype`)) +
ggtitle("Phylogeny of Acidobacteriota Phylum")
6.2.2 SubTrees: Ecosystem Subtypes
#count ecosystem subtypes per class of acidiobacterio
ecosystem_counts <- NEON_MAGs_metagenomes_chemistry_Acid %>%
group_by(`Ecosystem Subtype`, `Class`) %>%
summarize(count = n())## `summarise()` has grouped output by 'Ecosystem Subtype'. You can override using
## the `.groups` argument.#grab list of ecosystem subtypes
ecosystem_counts2<- ecosystem_counts %>%
group_by(`Ecosystem Subtype`) %>%
summarize(count = n())
ecosystemlist <- as.list(ecosystem_counts2$`Ecosystem Subtype`)
# Loop over each subtype
for (subtype in ecosystemlist){
NEON_MAGs_metagenomes_chemistry_subtype <- NEON_MAGs_Acidobacteriota %>%
filter(!is.na(Class)) %>%
filter(`Ecosystem Subtype` == subtype) %>%
filter(!is.na(`Ecosystem Subtype`))
MAGs_subtype_label <- NEON_MAGs_metagenomes_chemistry_subtype$label
tree_bac_MAGs_subtype <-drop.tip(tree_bac,tree_bac$tip.label[-match(MAGs_subtype_label, tree_bac$tip.label)])
p <- ggtree(tree_bac_MAGs_subtype, layout="circular") %<+%
NEON_MAGs_metagenomes_chemistry +
geom_point(mapping=aes(color=Class)) +
ggtitle(label= paste("Ecosystem Subtype Profile:", subtype, subtitle="Acidobacteriota"))
print(p)
}
6.2.3 Prevalence
Now let’s take a look at the numbers and visualize counts of Acidobacteriota data per class and ecosystem subtype. Here, we see overwhelmingly that the Terriglobia Class is overrepresented overall, and especially makes up most bacteria classes within Shrublands, Temperate Forest, Tundra, Boreal Forest, and Grasslands Ecosystems. Terriglobia is absent in Tropical Forest and Wetland ecosystems entirely, as are most Acidobacteriota, given by very low count values.
ggplot(ecosystem_counts, aes(y=`Ecosystem Subtype`, x= count, fill = `Class`)) +
geom_bar(stat="identity")+
ggtitle("Acidobacteriota", subtitle = "Prevalance per Ecosystem Subtype, partioned by Class designations") +
theme_classic()
### Genus-Class Biodiversity Lets look at a finer biodiversity metric
and observe the prevalence of Acidobacteriota genus per class.
Unsurprisingly, the most abundant class in Acidobacteriota is also the
most diverse. Terriglobia is the most prevalent in the Shrublands and
Termperate Forest, and is absent in the Wetlands and Tropical Forest.
The Wetlands and Tropical Forest consist of Acidobacteriota in the
classes Blastocatellia, Thermoanaerobaculi, and Vicinamibacteria.
Interestingly, it appears that genuses within Wetlands and Tropical
Forest within the Blastocatellia class do not overlap. Additionally, the
Tundra ecosystem, arguably equalloy as hostile as Desert, maintains an
biodiverse array of Acidobacteriota within the Terriglobia.
ecosystem_counts3 <- NEON_MAGs_metagenomes_chemistry_Acid %>%
group_by(`Ecosystem Subtype`, `Class`, `Genus`) %>%
summarize(count = n())## `summarise()` has grouped output by 'Ecosystem Subtype', 'Class'. You can
## override using the `.groups` argument.ggplot(ecosystem_counts3, aes(y=`Ecosystem Subtype`, x=count)) +
geom_bar(aes(fill=`Genus`), stat="identity") +
guides(fill = FALSE)+
facet_grid(~Class)+
ggtitle("Acidobacteriota", subtitle = "Genus per Ecosystem Subtype, facetted by Class designations") +
theme_classic()## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Shrubland x Acidiobacteriota Here we can inspect the phylogeny of
Acidiobacteriota at the genus level within the Shrubland biome and
investigate assembly type and bin completeness (vertical bars)
indicating number of unique single copy genes (SCGs) present within the
bin / the number of unique SCGs in the list. It is evident that the
shrubland biome harbors 40 different bacterial genuses.
#make a tree specific to the Shurbland Tundra
##Filter from Shrubland only within the Acidiobacteriota Clade
NEON_MAGs_metagenomes_chemistry_Shrub <- NEON_MAGs_Acidobacteriota %>%
filter(!is.na(Class)) %>%
filter(!is.na(Genus)) %>%
filter(`Ecosystem Subtype` == "Shrubland") %>%
filter(!is.na(`Ecosystem Subtype`)) %>%
rename("AssemblyType" = "Assembly Type") %>%
rename("BinCompleteness" = "Bin Completeness") %>%
rename("BinContamination" = "Bin Contamination") %>%
rename("TotalNumberofBases" = "Total Number of Bases") %>%
rename("EcosystemSubtype" = "Ecosystem Subtype")
#tip labels
MAGs_Shrub_label <- NEON_MAGs_metagenomes_chemistry_Shrub$label
#drop everything that's not shrub
tree_bac_MAGs_Shrub <-drop.tip(tree_bac,tree_bac$tip.label[-match(MAGs_Shrub_label, tree_bac$tip.label)])
ggtree(tree_bac_MAGs_Shrub, layout="circular", branch.length="none") %<+%
NEON_MAGs_metagenomes_chemistry_Shrub +
geom_point2(mapping=aes(color=`Genus`)) +
new_scale_fill() +
geom_fruit(
data=NEON_MAGs_metagenomes_chemistry_Shrub,
geom=geom_tile,
mapping=aes(y=label, x=1, fill = NEON_MAGs_metagenomes_chemistry_Shrub$AssemblyType ),
offset=0.08, # The distance between external layers, default is 0.03 times of x range of tree.
pwidth=0.25 # width of the external layer, default is 0.2 times of x range of tree.
) +
labs(fill = "Assembly Type") +
new_scale_fill() +
geom_fruit(
data=NEON_MAGs_metagenomes_chemistry_Shrub,
geom=geom_col,
mapping=aes(y=label, x=NEON_MAGs_metagenomes_chemistry_Shrub$TotalNumberofBases),
pwidth=0.4,
axis.params=list(
axis="x", # add axis text of the layer.
text.angle=-45, # the text size of axis.
hjust=0 # adjust the horizontal position of text of axis.
),
grid.params=list() # add the grid line of the external bar plot.
) +
theme(#legend.position=c(0.96, 0.5), # the position of legend.
legend.background=element_rect(fill=NA), # the background of legend.
#legend.title=element_text(size=7), # the title size of legend.
#legend.text=element_text(size=6), # the text size of legend.
legend.spacing.y = unit(0.02, "cm")) +
ggtitle("Acidiobacteriota", subtitle= "Shrubland Biodiversity") ## ! The following column names/name: Site.x, Sample Name, Site ID.x, Subplot.x, Layer.x, Date.x, IMG Genome ID.x, Bin Quality, GTDB-Tk Taxonomy Lineage, Domain, Phylum, Class, Order, Family, Genus, Species, BinCompleteness, BinContamination, TotalNumberofBases, 5s rRNA, 16s rRNA, 23s rRNA, tRNA Genes, Gene Count, Scaffold Count, AssemblyType, taxon_oid, Study Name, Site.y, Site ID.y, Subplot.y, Layer.y, Date.y, IMG Genome ID.y, GOLD Study ID, Ecosystem, Ecosystem Category, EcosystemSubtype, Ecosystem Type, Specific Ecosystem, Latitude, Longitude, Genome Size * assembled, Gene Count * assembled, siteID, plotID, nlcdClass, decimalLatitude, decimalLongitude, elevation, collectionDate, horizon, soilTemp, d15N, organicd13C, nitrogenPercent, organicCPercent, CNratio, soilInWaterpH, soilInCaClpH are/is the same to tree data, the tree data column names are : label, y, angle, Site.x, Sample Name, Site ID.x, Subplot.x, Layer.x, Date.x, IMG Genome ID.x, Bin Quality, GTDB-Tk Taxonomy Lineage, Domain, Phylum, Class, Order, Family, Genus, Species, BinCompleteness, BinContamination, TotalNumberofBases, 5s rRNA, 16s rRNA, 23s rRNA, tRNA Genes, Gene Count, Scaffold Count, AssemblyType, taxon_oid, Study Name, Site.y, Site ID.y, Subplot.y, Layer.y, Date.y, IMG Genome ID.y, GOLD Study ID, Ecosystem, Ecosystem Category, EcosystemSubtype, Ecosystem Type, Specific Ecosystem, Latitude, Longitude, Genome Size * assembled, Gene Count * assembled, siteID, plotID, nlcdClass, decimalLatitude, decimalLongitude, elevation, collectionDate, horizon, soilTemp, d15N, organicd13C, nitrogenPercent, organicCPercent, CNratio, soilInWaterpH, soilInCaClpH.## ! The following column names/name: Site.x, Sample Name, Site ID.x, Subplot.x, Layer.x, Date.x, IMG Genome ID.x, Bin Quality, GTDB-Tk Taxonomy Lineage, Domain, Phylum, Class, Order, Family, Genus, Species, BinCompleteness, BinContamination, TotalNumberofBases, 5s rRNA, 16s rRNA, 23s rRNA, tRNA Genes, Gene Count, Scaffold Count, AssemblyType, taxon_oid, Study Name, Site.y, Site ID.y, Subplot.y, Layer.y, Date.y, IMG Genome ID.y, GOLD Study ID, Ecosystem, Ecosystem Category, EcosystemSubtype, Ecosystem Type, Specific Ecosystem, Latitude, Longitude, Genome Size * assembled, Gene Count * assembled, siteID, plotID, nlcdClass, decimalLatitude, decimalLongitude, elevation, collectionDate, horizon, soilTemp, d15N, organicd13C, nitrogenPercent, organicCPercent, CNratio, soilInWaterpH, soilInCaClpH are/is the same to tree data, the tree data column names are : label, y, angle, Site.x, Sample Name, Site ID.x, Subplot.x, Layer.x, Date.x, IMG Genome ID.x, Bin Quality, GTDB-Tk Taxonomy Lineage, Domain, Phylum, Class, Order, Family, Genus, Species, BinCompleteness, BinContamination, TotalNumberofBases, 5s rRNA, 16s rRNA, 23s rRNA, tRNA Genes, Gene Count, Scaffold Count, AssemblyType, taxon_oid, Study Name, Site.y, Site ID.y, Subplot.y, Layer.y, Date.y, IMG Genome ID.y, GOLD Study ID, Ecosystem, Ecosystem Category, EcosystemSubtype, Ecosystem Type, Specific Ecosystem, Latitude, Longitude, Genome Size * assembled, Gene Count * assembled, siteID, plotID, nlcdClass, decimalLatitude, decimalLongitude, elevation, collectionDate, horizon, soilTemp, d15N, organicd13C, nitrogenPercent, organicCPercent, CNratio, soilInWaterpH, soilInCaClpH, xmaxtmp.
7 Discussion
Using open-sourced high-performance computing techniques, we were able to extract useful information from genomic and metagenomic datasets from the National Ecological Observatory Network. By parsing and visualizing these data, we have demonstrated there’s a host of information which is important for ecological, epidemiological, and medicinal purposes. For instance, these data demonstrate that possible pathogenic bacteria can be monitored and traced in a relatively simple manner. We have found that the power of these studies can yield insight into possible outbreaks and be useful for early warning and monitoring, although the sequencing depth could be improved to provide more accurate lower-level taxanomic identification.
We can also use these data to study the environment and adaptations of organisms in extreme conditions, such as Thermoleophilia. One can compare and contrast identification of organisms such as these with others found in more conventional environments to identify key differences in their prefered ecological niches. This can be important not only for identification and tracking purposes, but could provide useful predictive powers as to how bacteria might adapt to a changing climate where local temperatures and pH might fluctuate.
8 Conclusion
Here, we have demonstrated to usefulness of open, crowd-sourced data analysis of large datasets. Using data gathered from samples collected by scientists in the field, individuals with very little specialized training can utilize platforms such as R to perform analysis of such data which in the past may have taken decades to sift through by individual scientists or small scientific teams. Using crowd sourcing for data analysis can also overcome limitations of an individual’s training by having data visualizations generated from an unbiased third party.